Saved Queries

by Xiaoming Ren, Hui Song, He Li, Yuanren Zhou, Muqing Li, Shihao Yang, Chenxi Liu, Yunxiang Lu, Xin Li, Nan Jiang and Kazuhito Nishimura

Materials 2026, 19(18), 3810; https://doi.org/10.3390/ma19183810 (registering DOI) - 8 Sep 2026

Abstract

Water-lubricated seal interfaces are particularly vulnerable to fluid-film instability, which accelerates interfacial friction and wear and compromises the reliability and service life of underwater equipment. Here, a novel graphite-microcrystalline diamond (Graphite-MCD) soft–hard seal-face tribopair was proposed and systematically benchmarked against SiC-SiC and Graphite-SiC tribopairs under high-pressure water lubrication. Their sealing capacity, friction and wear behavior, and interfacial evolution were evaluated. The polished Graphite-MCD pair achieved the highest limiting PcV (90.73 MPa·m·s⁻¹) and the lowest wear rate, markedly outperforming the SiC-SiC and Graphite-SiC pairs. Multiscale interfacial characterization showed that the SiC-SiC pair failed primarily through adhesive instability after breakdown of the water film. Although the Graphite-SiC pair reduced interfacial shear resistance, sliding generated a continuous triboreaction layer accompanied by third-body wear. In contrast, the Graphite-MCD pair preserved interfacial structural integrity and suppressed the formation of an unstable continuous reaction layer and abundant abrasive debris. This behavior enabled a synergistic combination of a low-shear lubricating interface and a stiff load-bearing counterface. These results demonstrate that improving the limiting PcV of mechanical seals cannot be achieved by reducing the friction coefficient alone; rather, it requires simultaneous control of lubricity and interfacial stability. The findings provide a mechanistic basis for designing soft–hard tribopairs for high-pressure, water-lubricated mechanical seals. Full article

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33 pages, 1824 KB

Open AccessSystematic Review

The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review

by George Katharakis, Nikolaos Rikos, Michael Rovithis, Dimitrios Papageorgiou and Areti Stavropoulou

Healthcare 2026, 14(18), 2887; https://doi.org/10.3390/healthcare14182887 (registering DOI) - 8 Sep 2026

Abstract

Background/Objectives: Healthcare service quality is a critical dimension of patient safety and operational performance. Traditional assessment approaches are often limited in their ability to support prediction, which has increased interest in machine learning (ML) models. This systematic review assessed the effectiveness of ML algorithms in predicting healthcare service quality metrics, with emphasis on their applications, comparative performance and implementation challenges. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, CINAHL, and ScienceDirect for studies published between 1 January 2020 and 30 July 2025. Following title/abstract screening and full-text review, 49 studies were included. Studies were grouped into conventional/ensemble ML and deep learning categories based on the primary model class analyzed in each article. Results: The reviewed studies focused mainly on acute clinical and operational outcomes, especially length of stay (27.0%), mortality (23.0%), and readmission rates (18.0%), while subjective, patient-centered metrics received less attention. Conventional and ensemble ML models, particularly RF and XGBoost, were frequently reported, while deep learning models were used in more complex prediction tasks. Conclusions: The evidence suggests that well-validated and interpretable ML models can support healthcare quality prediction. However, important challenges remain regarding implementation, validation, generalizability, and data heterogeneity. Full article

(This article belongs to the Special Issue Applications of Digital Technology in Comprehensive Healthcare)

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30 pages, 10767 KB

Open AccessArticle

How Are Green Financial Markets Linked to Green Cryptocurrency Return States? Evidence from a Cross-Quantilogram Approach

by Qiqi Gu, Junda Wu and Jian Yao

Mathematics 2026, 14(18), 3244; https://doi.org/10.3390/math14183244 (registering DOI) - 8 Sep 2026

Abstract

This paper examines directional quantile dependence from green bonds, clean energy markets, and carbon markets to the return states of five literature-classified green cryptocurrencies. Using daily returns from 25 September 2019 to 23 May 2025, we estimate static cross-quantilograms on a [...] Read more.

19 \times 19

quantile grid at lags 1, 5, and 22, 500-observation rolling cross-quantilograms, bootstrap surface tests, and green-specificity comparisons with five cryptocurrencies that used proof-of-work (PoW) consensus throughout the comparison sample. Point estimates display heterogeneous short-run patterns in selected green-bond and carbon-market pairs, but none of the 45 forward surfaces rejects the omnibus null at the 5% level. Rolling estimates vary across windows and tail cutoffs. The largest raw green-group contrast occurs for carbon quota prices at lag five, although its time-series bootstrap interval includes zero and factor-adjusted tests do not detect systematic green-minus-PoW separation. Descriptive quantile-on-quantile connectedness estimates are higher at extreme quantiles than at the median–median state. Overall, the evidence is more consistent with broad cryptocurrency-market conditions than with a uniform dependence pattern associated with the environmental label. Full article

(This article belongs to the Special Issue Modeling, Analysis and Optimization for Mathematical Finance, Economics and Risks, 2nd Edition)

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25 pages, 20981 KB

Open AccessArticle

A Geotechnical–Hydrogeological Property Zonation Approach for Landslide Hazard Modelling in the eThekwini Metropolitan Region, Eastern South Africa

by Sibonakaliso Goodman Chiliza, Egerton D. C. Hingston and Molla Demlie

GeoHazards 2026, 7(4), 110; https://doi.org/10.3390/geohazards7040110 (registering DOI) - 8 Sep 2026

Abstract

Rainfall-induced landslides pose a significant threat to communities and infrastructure in the eThekwini Metropolitan Region, South Africa. This paper presents a geotechnical–hydrogeological property zonation and parameterisation framework developed to support future physically based slope stability modelling. Using a weighted sum analysis in a GIS environment, the landscape was subdivided into distinct property zones by integrating lithology, slope gradient, and landform, with weights derived from a fully reproducible renormalisation of a previously published regional frequency ratio (FR) susceptibility model. This procedure provided the foundation for assigning zone-specific parameters, including effective shear strength parameters (c′ and ϕ′) and saturated hydraulic conductivity (Ksat), derived from laboratory testing, borehole pump testing analysis, and empirical relationships. The approach delineated four geotechnical–hydrogeological zones. A correlation of these zones against an inventory of 819 landslides revealed that over 82% of failures have occurred within Zones 2 and 3. While the hydrogeological conditions of these two susceptible zones range from intermediate to low permeability (K_sat = 10⁻⁵ to 10⁻⁸ m/s), which promotes transient pore pressure build-up, their high failure frequency corresponds closely with shared low shear strength (c′ = 5 kPa) and comparatively low effective friction angle (ϕ′ = 27.5–30°). This identifies shear strength as an important predisposing control on instability, relative to the inherently more stable Zones 1 and 4 (c′ = 10–15 kPa, ϕ′ = 30–35°). Consequently, this zonation and parameterisation approach advances landslide hazard assessment by providing a reproducible, model-ready dataset intended for future transient rainfall-infiltration simulations (e.g., TRIGRS), laying the groundwork for physically based early-warning systems, and supporting risk-informed urban development. Full article

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12 pages, 494 KB

Open AccessReview

Molecular Mechanisms Between Canine Parvovirus 2 and Host Interactions: From Viruses to Innate Immune Signaling Pathways

by Guanyu Zhao, Xinying Yang, Yuehua Li, Muzi Li, Shiping Song, Jinghan Wang, Cong Liu, Zequn Dong, Zhenhua Gong and Junhui Liu

Viruses 2026, 18(9), 985; https://doi.org/10.3390/v18090985 (registering DOI) - 8 Sep 2026

Abstract

Canine parvovirus 2 (CPV-2) is a major pathogen causing acute haemorrhagic enteritis and myocarditis in dogs. Since the discovery in the late 1970s, the original CPV-2 strain has evolved through VP2 mutations into new subtypes CPV-2a, 2b, and 2c. The viruses pose a persistent threat to canine health worldwide. This review summarizes the innate immunity evasion tactics employed by CPV-2, focusing on the interactions of various viral proteins and host signaling pathways. CPV-2 regulates the activation and inhibition of Toll-like receptors (TLRs), thereby influencing the downstream MyD88/TRIF signaling pathway and modulating the activation of NF-κB and interferons. CPV-2 exerts bidirectional regulation of apoptosis, thereby modulating host innate immune responses and promoting viral replication. CPV-2 employs multiple strategies to evade the humoral and adaptive immune systems, including evolutionary escape through rapid replication and the accumulation of mutations. Clarifying the key mechanisms underlying the defective TLR activation, the immunomodulatory functions of CPV proteins, and cross-species transmission of CPV-2 among animals and the potential zoonotic risk to humans can provide a crucial theoretical basis for precise virus control, understanding immune evasion, and assessing zoonotic risks, with significant value for both vaccine development and basic research. Full article

(This article belongs to the Special Issue Spotlight on Bocavirus and Other Parvoviruses, and Overlooked Respiratory Viruses)

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25 pages, 8713 KB

Open AccessReview

Comprehensive Review of the Machining of Incoloy 800

by Ganesha Prasad, Vijay G. S. and Raghavendra C. Kamath

Eng 2026, 7(9), 459; https://doi.org/10.3390/eng7090459 (registering DOI) - 8 Sep 2026

Abstract

Heat-resistant superalloys (HRSA) offer good oxidation, corrosion, and creep resistance and exhibit better rupture strength characteristics. These HRSA are used under cyclic loads and high-temperature environments such as turbine blades, pressure vessels, turbocharger rotors, seals, boilers, and high-temperature fasteners. Inconel 718, Inconel 825, Nimonic 80A, Nimonic C263, Incoloy/Inconel 800, and Inconel 617 are some of the most used superalloys. Despite several advantages and applications, poor thermal conductivity, a tendency towards work hardening, and outstanding metallurgical consistency all adversely affect the machining of these alloys. This study reviews the various machining possibilities for Incoloy 800 superalloys to date. The present article aims to investigate multiple conventional and non-conventional machining studies on Incoloy 800 in terms of machining conditions, surface roughness (SR), Tool wear (TW), and the optimization of process parameters. Furthermore, this article summarizes the developments in machining Incoloy 800 over the past few years and identifies a prominent research gap for future studies. Full article

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28 pages, 3515 KB

Open AccessArticle

Delay-Induced Stability Switching and Optimal Control of an Information Propagation Model with Information-Holding Behavior

by Rongyu Zhang, Xinwen Zhang and Xuechao Zhang

Mathematics 2026, 14(18), 3243; https://doi.org/10.3390/math14183243 (registering DOI) - 8 Sep 2026

Abstract

People who benefit from valuable information do not always pass it on. We develop a delayed IHSCR information propagation model in which a beneficiary can either continue spreading the information or hold it after a behavioral decision lag. The key modeling distinction is that information acquisition and the subsequent sharing-or-holding decision are treated as separate behavioral stages, while information holders can also suppress active spreaders. The delayed transitions are written as outflow rates, so arbitrary nonnegative histories do not automatically preserve positivity. We therefore work with nonnegative-feasible histories and show that such histories exist near each positive equilibrium on any fixed finite interval. For zero delay, we derive the basic reproduction number, prove global asymptotic stability of the information-free equilibrium when

R_{0} < 1

, and give Routh–Hurwitz conditions for local stability of the positive equilibrium. With the delay as a bifurcation parameter, the linearized system gives a transcendental characteristic equation and a quartic frequency equation. The critical delay is recovered from an atan2-based phase condition, and the transversality condition identifies the first spectral stability switch. For the stability-switching parameter set, an independent characteristic-root computation verifies a unique simple crossing, and a characteristic-matrix normal-form calculation gives a negative first Lyapunov coefficient, classifying the local Hopf bifurcation as supercritical with a locally orbitally stable periodic branch. We also prove the existence of a delayed optimal control on the nonnegative-feasible set and derive the optimality system, including advanced adjoint terms. At the baseline cost weights, the computed dynamic control gives a modestly higher net objective than a numerically optimized constant-control benchmark, and this ordering persists when the relative cost ratio

c_{1} / c_{2}

is varied from 1 to 100. A two-parameter sensitivity analysis shows how the beneficiary-to-spreader and beneficiary-to-holder transition rates shift the first critical delay. Full article

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27 pages, 959 KB

Open AccessReview

From Colonies to Copies: Integrating PCR, Culture, and AST in Bacterial Diagnostics

by Rob E. Carpenter, Andrew Krouse and Alaina Vincent

Appl. Microbiol. 2026, 6(9), 107; https://doi.org/10.3390/applmicrobiol6090107 (registering DOI) - 8 Sep 2026

Abstract

Real-time polymerase chain reaction (PCR) has reshaped bacterial infectious disease diagnostics, yet important interpretive gaps remain regarding the relationship among molecular detection, cycle threshold (Ct) values, microbial viability, culture findings, and phenotypic antimicrobial susceptibility. In particular, Ct values are frequently overinterpreted as direct surrogates for viable bacterial burden, and PCR–culture discordance may be interpreted without sufficient consideration of the distinct biological information provided by each method. This review therefore aims to clarify the complementary biological and analytical roles of PCR and bacterial culture, critically examine the determinants and limitations of Ct interpretation and PCR–culture discordance, and provide a practical framework for integrating molecular detection, culture, and antimicrobial susceptibility testing (AST) into clinically and stewardship-informed decision-making. The genotypic lens of PCR (detection of target nucleic acid and resistance genes) is contrasted with the phenotypic lens of bacterial culture and AST, emphasizing that genotype and phenotype distinguish biological layers and account for common PCR–culture discordance. Evidence on Ct variability, assay design, inhibition, and panel scope is synthesized to demonstrate why Ct is inherently assay-specific and non-portable across platforms. Accordingly, MIQE-aligned quality safeguards and assay-specific principles are presented to guide the interpretation of Ct values. Bedside decision tables then integrate Ct patterns, specimen sterility, and patient acuity to support treatment, observation, or additional testing. As a narrative review of heterogeneous evidence, this synthesis does not provide pooled estimates or a uniform risk-of-bias assessment. The proposed Ct categories and clinical framework should therefore be viewed as assay-specific guidance, not universally validated thresholds. Finally, a stepwise workflow is outlined in which PCR is used for rapid rule-in, while culture and AST are retained for confirmation, de-escalation, and dosing. This integrated approach reframes Ct as a qualified signal rather than a standalone truth, supporting faster yet biologically grounded and stewardship-consistent infectious disease management. Full article

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17 pages, 675 KB

Open AccessArticle

First European Identification of a Partial Tacheng Tick Virus 8-like Underscores the Hidden Burden of Tick-Borne Flavivirus-like Viruses

by Guilherme Moreira, Eliane Silva, Joana Mourão and João R. Mesquita

Viruses 2026, 18(9), 984; https://doi.org/10.3390/v18090984 (registering DOI) - 8 Sep 2026

Abstract

Tick-associated viruses are an underexplored component of global viromes, and highly divergent RNA viruses often remain undetected due to low abundance and sequence divergence. Tacheng tick virus 8 (TcTV8) is a poorly characterized RNA flavivirus-like virus originally reported from Dermacentor-associated ticks in China, with few subsequent reports and no confirmed detections outside Asia. Here, we report the detection and partial genomic characterization of a TcTV8-like virus in ticks collected from Portugal. Sequence-independent (SISPA) nanopore sequencing of individual ticks recovered three partial fragments (~2.7 kb, ~14% of the reference genome; ~30% of the reference covered at ≥1×), which shared high amino-acid identity with the TcTV8 polyprotein, including a methyltransferase-region domain. Phylogenetic analysis placed the Portuguese sequence within the TcTV8 lineage. Read-level classification and coverage analysis further support the presence of this virus in the positive sample. These findings represent, to our knowledge, the first detection of a TcTV8-like virus in European ticks, extending its known geographic range beyond Asia. The detection of this cryptic viral lineage highlights the need for broader tick virome surveillance to better understand the diversity, evolution, and ecology of flavivirus-like viruses. Full article

(This article belongs to the Special Issue Synanthropy and Zoonotic Viral Spillover)

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26 pages, 4877 KB

Open AccessReview

Strategic Human Resource Management, Employee Well-Being, and Talent Retention in Family-Run Digital Tourism and Hospitality SMEs: A Conceptual Framework

by Dorothea Kossyva and Nikolaos Georgopoulos

Merits 2026, 6(3), 25; https://doi.org/10.3390/merits6030025 (registering DOI) - 8 Sep 2026

Abstract

Family-run small and medium-sized enterprises (SMEs) dominate the tourism and hospitality sector, and a growing number are digitalizing while retaining their family character. The present study develops a multi-level conceptual framework explaining how strategic human resource management (SHRM) practices support talent retention in these firms under digital transformation. Three research streams bear on the question, namely strategic HRM and employee well-being, socioemotional wealth in family business, and digital transformation in tourism and hospitality, and each rests on an assumption that the other two contradict in this setting, concerning respectively the formality of the HR system, the stability of the relational channel of delivery, and the irrelevance of ownership structure to how employees experience technology. Drawing on the deliberate–emergent strategy distinction and on the separation between intended, implemented, and perceived HR practices, the study argues that strategic HRM in these firms is emergent rather than absent. Seven propositions are formulated from job demands–resources and socioemotional wealth theory, according to which SHRM practices support retention through employee well-being, family involvement strengthens this conversion, digital transformation weakens it, the two organizational forces interact, and the external labor environment conditions the outcome. The framework identifies the mechanism through which digitalization erodes the HR advantage of family firms, which rests on the channel of implementation rather than family ownership as such. Full article

(This article belongs to the Special Issue Digital Entrepreneurs in Tourism and Hospitality: Human Resources Challenges and Opportunities in the Tech Era)

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25 pages, 35474 KB

Open AccessArticle

Confusion-Resistant Learning for Few-Shot Oriented Object Detection in Aerial Images

by Yang Yang, Lingjun Li, Yuanhang Wang, Chao Qi, Linlong Peng and Xiwen Yao

Remote Sens. 2026, 18(18), 3062; https://doi.org/10.3390/rs18183062 (registering DOI) - 8 Sep 2026

Abstract

This study is devoted to few-shot oriented object detection in aerial images, aiming to enhance detection performance for novel object classes, using only limited supervised samples. Currently, most few-shot object detection models adopt the two-stage fine-tuning approach (TFA), which consists of a base training stage and a few-shot fine-tuning stage. However, the region proposal network (RPN) suffers from foreground–background classification confusion and the rotation angle ambiguity of the square-like bounding boxes. These issues significantly degrade the detection performance for novel categories. To this end, we propose a confusion-resistant learning (CRL) for few-shot aerial oriented object detection. CRL contains a classification reweighting scheme (CRS) and an Edge-Vectors Cosine Similarity (EVCS) Loss. First, the CRS utilizes credible bounding box regression outputs from the base training stage to assist foreground–background classification learning. This process suppresses classification confusion and improves accuracy for novel categories. Second, we propose an EVCS Loss, which builds upon the Kalman filtering IoU (KFIoU) loss. The EVCS Loss alleviates rotation angle confusion for square-like boxes by maximizing the cosine similarity between the edges of the ground-truth and predicted bounding boxes. In addition, CRL can be plugged into the existing two-stage oriented object detectors. Extensive experiments on DOTA and DIOR-R oriented object detection benchmarks show that, compared with the ReDet-KFIoU baseline, our CRL achieves up to 2.4% overall AP50 improvement and 1.8% novel-class AP50 improvement on DOTA, and yields up to 2.0% overall AP gain and 2.3% novel-class AP gain on DIOR-R, providing direct quantitative evidence for the effectiveness of our method. Full article

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12 pages, 526 KB

Open AccessArticle

Life Satisfaction and Mindfulness in Esports-Involved Gamers in Türkiye: Parallel Mediation of Problematic Online Gaming and Maladaptive Daydreaming

by Seyhan Bekir

Behav. Sci. 2026, 16(9), 1595; https://doi.org/10.3390/bs16091595 (registering DOI) - 8 Sep 2026

Abstract

The present study explores the parallel mediating roles of problematic online gaming and maladaptive daydreaming on the relationship between life satisfaction and mindfulness in esports-involved gamers in Türkiye. This research area has attracted growing scholarly interest due to its potential implications for esports-involved gamers’ mindfulness and life satisfaction. The present study employed a parallel multiple mediation model to investigate the indirect roles of these two mediators in the association between life satisfaction and mindfulness in a sample of esports-involved gamers. A total of 480 esports-involved gamers completed an online survey comprising the Satisfaction with Life Scale, the Mindful Attention Awareness Scale, the Internet Gaming Disorder Scale—Short Form, and the Maladaptive Daydreaming Scale. The data were analyzed using correlation analyses and Hayes’ PROCESS macro (Model 4) with bootstrapping to test the hypothesized mediation model. The findings of the study demonstrated correlations between all these variables. Mediation analyses indicated that both problematic online gaming and maladaptive daydreaming mediated the relationship between life satisfaction and mindfulness, as the direct roles became non-significant once the mediators were included. Interventions designed to enhance mindfulness in esports-involved gamers should therefore address this to decrease maladaptive daydreaming. Full article

(This article belongs to the Special Issue Digital Health and Well-Being: Navigating Risks and Building Resilience)

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22 pages, 3314 KB

Open AccessReview

Andrographolide: Mechanisms and Therapeutic Potential in Alzheimer’s and Parkinson’s Disease

by Angélica Ríos-Gallardo, Daniela Herrera-Ramirez, Sussy Bastias-Candia and Nibaldo C. Inestrosa

Molecules 2026, 31(18), 3139; https://doi.org/10.3390/molecules31183139 (registering DOI) - 8 Sep 2026

Abstract

Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are characterized by the progressive loss of specific neuronal cell populations and are associated with protein aggregates. Current therapeutic approaches are still limited due to the complexity and heterogeneity of these diseases, which points toward an urgent need to discover and develop new therapeutic agents. Natural compounds are a promising source of novel bioactive agents targeting multiple mechanisms of action implicated in neurodegeneration. Andrographolide (ANDRO) is a natural compound extracted from Andrographis paniculata, a traditional Chinese herb known for its anti-inflammatory and antioxidant properties, which has emerged as a potential neuroprotective agent due to its ability to cross the blood–brain barrier (BBB). ANDRO can exert neuroprotective effects by modulating numerous transcription factors and signaling pathways across different cell types in the central nervous system (CNS). It has been described that ANDRO reverses cognitive and/or motor impairments in AD and PD study models. However, the cellular and molecular mechanisms behind these protective effects are still being elucidated. In this review, we analyze the most recent findings on ANDRO, a neuroprotective agent with multiple biological targets that could reduce the progression of the most prevalent neurodegenerative diseases, AD and PD. Full article

(This article belongs to the Special Issue Small Molecule Drug Discovery and Development to Face Neurodegenerative Threats)

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26 pages, 15338 KB

Open AccessArticle

An Interpretable BO-TCBDA Deep Learning Framework for Winter Wheat Yield Estimation Using Multi-Source Remote Sensing Data

by Anqi Xue, Shufang Tian and Tingyan Fu

Remote Sens. 2026, 18(18), 3061; https://doi.org/10.3390/rs18183061 (registering DOI) - 8 Sep 2026

Abstract

Reliable crop yield estimation is fundamental to food security and efficient agricultural management. However, current deep learning models still face limitations in selecting and integrating multi-source features, and their high predictive accuracy is often accompanied by limited interpretability. This study introduces a Bayesian Optimization–Temporal Convolutional Network–Bidirectional Long Short-Term Memory–Dual Attention (BO-TCBDA) deep learning framework for winter wheat yield estimation. Using Henan Province, China, as the study area, county-level winter wheat yield from 2013 to 2022 was estimated using the Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and climate data. The proposed model was compared with five commonly used machine learning and deep learning models. BO-TCBDA achieved the best performance, with an R² of 0.823 and an RMSE of 561.26 kg/ha. SIF improved the predictive performance of all models, with statistically significant gains observed in the deep learning models. The dual-attention mechanism provided interpretable insights by revealing relatively balanced contributions among the input features and highlighting the grain-filling stage through temporal attention. Furthermore, SHAP-based cross-validation analysis identified T12, corresponding to the latter part of the jointing stage, as the period with the highest contribution to yield prediction. The model also achieved an R² of approximately 0.80 about 25 days before harvest. Overall, BO-TCBDA provides an accurate and interpretable approach for county-level winter wheat yield estimation and supports regional food security assessments and precision agriculture. Full article

(This article belongs to the Special Issue AI-Enabled Precision Agriculture for Crop Monitoring and Yield Prediction Using Remote Sensing Technologies)

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19 pages, 10138 KB

Open AccessArticle

Computational Interpretation of Functional Divergence of VOC Family Catechol Dioxygenases in Bacillus thuringiensis HHY919: Insights from Homology Modeling and Molecular Docking

by Liwei Yang, Hongyan Hou, Wenjie Zhang, Weibing Zhang, Wei Zhang, Yulong Zhao, Feier Ren, Shuaijiang Guo and Zhonghao Wang

Catalysts 2026, 16(9), 811; https://doi.org/10.3390/catal16090811 (registering DOI) - 8 Sep 2026

Abstract

Catechol 2,3-dioxygenase (C23O) is the rate-limiting enzyme in the meta-cleavage pathway of aromatic compound degradation, yet its functional annotation within the structurally conserved VOC superfamily remains challenging due to high sequence homology. Here, we isolated a catechol-degrading strain from bovine feces and identified it as Bacillus thuringiensis HHY919 via whole-genome sequencing. Genome annotation revealed four VOC genes sharing the same COG annotation (“catechol 2,3-dioxygenase”) but divergent KO annotations (two as glyoxalases and two as C23O), suggesting functional divergence. Using homology modeling and molecular docking, we compared their binding affinities toward catechol and 11 derivatives. All four proteins showed typical meta-cleavage binding energies (−4.9 to −5.4 kcal/mol), with slightly more favorable binding than an ortho-cleavage control. Notably, gene4224 exhibited the broadest and strongest predicted affinities in virtual screening against 212 compounds, particularly for trichlorophenol (−6.0 kcal/mol) and complex natural products (qvina_score ≤ −7.6 kcal/mol). Phylogenetic analysis and docking results jointly identified gene3355 and gene4224 as computationally prioritized C23O candidates, with gene4224 recommended as the top candidate for future enzyme engineering and bioremediation studies. This study provides a computational workflow for resolving functional ambiguity in VOC family enzymes and generating testable hypotheses for experimental validation. Full article

(This article belongs to the Special Issue 15th Anniversary of Catalysts: The Development and Future of Computational Catalysis)

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